Animal-vehicle collisions during the COVID-19 lockdown in early 2020 in the Krakow metropolitan region, Poland

The interrelations between human activity and animal populations are of increasing interest due to the emergence of the novel COVID-19 and the consequent pandemic across the world. Anthropogenic impacts of the pandemic on animals in urban-suburban environments are largely unknown. In this study, the temporal and spatial patterns of urban animal response to the COVID-19 lockdown were assessed using animal-vehicle collisions (AVC) data. We collected AVC data over two 6-month periods in 2019 and 2020 (January to June) from the largest metropolis in southern Poland, which included lockdown months. Furthermore, we used traffic data to understand the impact of lockdown on AVC in the urban area. Our analysis of 1063 AVC incidents revealed that COVID-19 related lockdown decreased AVC rates in suburban areas. However, in the urban area, even though traffic volume had significantly reduced, AVC did not decrease significantly, suggesting that lockdown did not influence the collision rates in the urban area. Our results suggest that there is a need to focus on understanding the effects of changes in traffic volume on both human behaviour and wildlife space use on the resulting impacts on AVC in the urban area.


Results
A total of 1063 AVC incidents involving avian (hereafter birds) and 15 wildlife mammalian species were recorded during the study period (see Table 1). As it was not possible to distinguish between smaller bird species, we grouped birds into a taxonomic group, which comprised 170 AVC cases. As for mammals, roe deer (Capreolus capreolus) (n = 519), red fox (Vulpes vulpes) (n = 117), wild boar (Sus scrofa) (n = 88), northern white-breasted hedgehog (hereafter hedgehog; Erinaceus roumanicus) (n = 62), brown hare (Lepus europaeus) (n = 35) and red squirrel (Sciurus vulgaris) (n = 21) were most frequently involved in road collisions in Krakow, Wieliczka and Niepolomice AVC patterns in urban versus suburban areas. To measure variation in AVC across the urban-suburban gradient, Multidimensional Scaling (MDS) was performed with the Jaccard index. The overall variation in AVC was distinct between urban and suburban communes (Fig. 1a). By Canonical Correlation Analysis (CCA), we found that the location factor explained 12.91% of the variance in the dataset (n = 36), separating the urban commune of Krakow from the suburban communes of Niepolomice and Wieliczka. The pairwise PERMANOVA found a significant difference (p ≤ 0.05) of variation in AVC between urban and suburban communes, but not significant between suburban communes (Fig. 1a). We found an overall decrease in AVC between 2019 and 2020 in suburban communes, but the AVC rates were similar in Krakow between 2019 and 2020 (Fig. 1b). AVC for red squirrel and European badger (Meles meles), wild boar, red fox, roe deer, hedgehog, birds, rats (Rattus spp.) and brown hare, were significantly higher in Krakow than in Niepolomice and Wieliczka (Figs. 1c, 2) based on pairwise comparative analysis of AVC among locations. Table 1. The total and mean (± SD) animal-vehicle collision (AVC) number during non-lockdown (January, February and June) and lockdown periods (March, April and May) recorded in the study area. SD-Standard deviation.

Commune
Year Period Total AVC Mean AVC (per month) SD www.nature.com/scientificreports/ Interestingly, we found a trend in the distribution of total AVC along the time range in different locations. By LOESS fitted line plot (Fig. 3) we found that total AVC were generally higher around 08:00 h and spread until 20:00 h.
Traffic in Krakow and its impact on AVC. The mean (± SD) number of vehicles per hour between January and June in 2019 was 24,116 (± 11,827.78), whilst in the same period in 2020 it was 20,891 (± 5839.99). The mean number of traffic volume during the lockdown months (March-May) in 2020 was 30,280, while for the same months in 2019, the traffic volume was 43,555. Traffic volume in Krakow decreased significantly during lockdown months (Fig. 4a, b) with April had the highest mean difference in traffic volume (Fig. 4c) between 2019 and 2020. However, despite significantly lower traffic, the total AVC in Krakow neither decrease significantly overall nor during the lockdown (Fig. 4c). The Spearman's correlation analysis (r s = 0.265; p = 0.06) between the mean difference in traffic volume and the mean difference in AVC comparing the two years found a weak relationship (Fig. 4d).   Figure 2. Distribution of AVC (y-axis) of the species with significant difference between the urban and suburban locations over the years (shape fill). The boxes represent the 25th, 50th (median) and 75th percentiles of the data; the whiskers represent the lowest (or highest) datum within 1.5 × interquartile range from the 25th (or 75th) percentile. The horizontal red line indicates the overall mean % of AVC of the specific animal. www.nature.com/scientificreports/ Spatial patterns change of AVC. The spatial pattern of AVC analysed independently for pre-lockdown months (January-February), lockdown months (March-May), and for June differed substantially. The pre-lockdown months in 2020 had a higher number of AVC incidents located in the proximity of forest habitats, in the western and eastern part of the study area in 2020 in comparison to 2019 (Fig. 5). During March-May, AVC were broadly similar between 2019 and 2020 throughout the study area with very minor exceptions in suburban areas in the south. AVC changes in June, by contrast, showed an increase in incidents in 2020 as compared to 2019 in the southern and northern part of the study area, while the east of the study area had a slight decrease in AVC incidents (Fig. 5).

Discussion
In this study, the urban commune of Krakow had the highest frequency of AVC regardless of the study period (lockdown vs non lockdown). In the suburban communes of Niepolomice and Wieliczka communes, AVC incidents were generally less frequent than in Krakow and were significantly lower during the lockdown period. Our results found that many animal species (i.e., wild boar, red fox, birds, roe deer, hedgehog, brown hare, red squirrel, European badger and rat) were involved in collisions more in the urban study area in comparison to the suburban areas even during lockdown. High rates of AVC may be attributed to several animal-related factors such as behavioural including changes in daily and seasonal activity associated with breeding, foraging or dispersal patterns and also habitat preferences with preferred road-crossing routes 27,28 . Specific foraging behaviour by various species is commonly associated with higher mortality rate on roads as by regularly feeding on roadsides animals are at a greater risk of collisions with vehicles. Most European wild ungulates are forest-dwelling species and are often known for being attracted to roadsides and regularly using road verges searching for specific minerals and plant material 28,29 . Similarly, some predator species frequently forage on roadkill 27,30 and predate upon prey using road verges as refuges 31 . Consistent with other studies, we found that AVCs were more pronounced during the early morning hours in Krakow [31][32][33] , and this trend retained during lockdown. Temporal activity rhythms of species could have a strong influence on the probability of AVC. The majority of mammalian species are crepuscular or nocturnal and are at higher risk of vehicle collision especially from dusk till midnight, and at dawn 34,35 . The most common conflictual wild animals in our study area are wild boar, roe deer and medium sized carnivores such as red fox 36 . On the other hand, roe deer did not show any significant difference in AVC in the three study sites between lockdown and non-lockdown over the 24 h period. In suburban areas, wild boar and red fox were involved in AVC for a shorter time span during lockdown compared to non-lockdown period. This is probably due to the behavioural plasticity in activity patterns that red foxes 37 and wild boars 38 exhibit in their native range, which allows both species to adapt to environmental changes. On the contrary, road crossings by roe deer are mainly driven by their behavioural patterns 39,40 rather than directly by the volume of traffic on a road 41 .
Finally, we found no significant differences in total AVC between lockdown and non-lockdown periods in Krakow, even though there was a significant reduction in traffic volume during lockdown months. Krakow city can be subject to increased animal activity when human impacts are reduced, due to several migration corridors   www.nature.com/scientificreports/ such as the Vistula River that is of regional and European importance, as well as favourable land cover (i.e., western wedge of greenery) 42 . Similar results were found in other studies 22,43 where AVC did not decrease in all studied regions during COVID-19 related travel restrictions. The relationship between traffic volume and the number of AVC has led to divergent conclusions being presented in studies published thus far. Some studies have found a positive relationship between traffic volume and AVC 44,45 , indicating high road kills during high traffic volumes, while few studies did not find any strong effects [46][47][48] . Thus, there may not always be a linear relationship between traffic volume and AVC 49 . Our study did not find a strong correlation between traffic volume and AVC in Krakow in 2020 compared to 2019. We therefore suggest that two possibilities for this result. Firstly, animals within the study area responded to lockdown by continuing their activity and movement within urban environments, which resulted in relatively comparable levels of AVC despite the decrease in traffic volume. This explanation is consistent with accounts of various wildlife species making use of human spaces during the pandemic 9 . As such, the decrease in road traffic during the pandemic might have caused certain species of wildlife to tolerate the risks associated with roads to access the benefits of roads and roadsides 43 . On the other hand, the lack of a strong correlation between traffic volume and AVC rates could also be attributed to human driving behaviour rather than animal behaviour alone 43 . In a regional study in Poland 26 , a decrease in drive time was observed across the high-speed roads during the pandemic, which are normally busy during peak hours. Consequently, reduction in traffic volume and empty roads encouraged high speeding during the pandemic 50 . Seiler and Helldin 51 have argued that low traffic volume coupled with greater vehicular speed can lead to higher animal mortality rates, even during lockdown 43 . This is due to the longer time interval between subsequent rapidly approaching vehicles, which stimulates an animal to attempt road crossings, thereby increasing the likelihood of collision 52 . Therefore, intermediate traffic volumes may result in higher rates of collisions than large traffic volumes because animals may be more willing to attempt to cross roads and highways with moderate than high traffic volume 49 . Furthermore, increased level of stress 53 or higher alcohol consumption during the pandemic in Poland 54 could impair driving thereby affecting collision risks 55 . Thus, a greater understanding of human driving behaviour would also help explain our findings regarding changes in traffic patterns during the pandemic in the urban area.

Conclusion
In this study we found that a high frequency of AVC existed in our urban study area, even during the lockdown, and that reduced traffic volumes in the city did not correspond to reductions in AVC. Considering these findings within the context of reduced human activity during COVID-19 restrictions, our results suggest that local lockdown measures have had a limited impact on AVC levels within this urban study area. Given the high levels of AVC that were found in this study, the importance of collecting high quality multi-species data, having reliable reporting systems 49 including the use of AVC hotspot mapping, reduced speed limits, speed bumps and warning road signage in key areas to reduce AVC as a source of human wildlife conflict 56 . The term 'anthropause' was suggested by Rutz et al. 9 as a label for Covid-19 lockdown, and researchers immediately noted this as a unique opportunity to study human impacts on the biosphere and the Earth's physical systems 57 . However, the anthropause is also a symbolic and cultural event that might affect how people and governments perceive and act on environmental challenges once the crisis phase of the pandemic has passed 58 . In our study, within the temporal and spatial scales investigated, we suggest that anthropause has impacted urban wildlife populations through a behavioural and ecological 'release' mechanism that has increased species' utilisation of urban habitats, but this release has had limited population effects as mortality, as measured by AVC, was independent of whether the anthropause had occurred. Therefore, there was likely little impacts on biodiversity in relation to AVC within our study area.

Methods
Study area. The study was conducted in southern Poland within the Krakow metropolitan area that comprises three communes of Krakow, Wieliczka, and Niepolomice, representing various levels of urbanisation (Fig. 6, Table 2). The urban commune of Krakow has an area of 327 km 2 and a human population of 779,115. It is an important transportation hub for major national and international roads, and is bisected by the Vistula River, a natural migration corridor for many wildlife species 59 . In terms of Krakow's landcover, built-up and urbanised areas constitute over 45% of the city, with 44% of the city used for agricultural purposes including crops, orchards, meadows and pastures.
Wieliczka and Niepolomice are mainly agricultural and forested suburban communes neighbouring Krakow (Fig. 6), each covering an area of approximately 100 km 2 , with a human population of 60,781 and 29,141 citizens, respectively. In terms of land cover, built-up and urbanised areas constitute approximately 20% and agricultural land constitutes almost 70% in both suburban communes ( Table 2). www.nature.com/scientificreports/ Wild animals are common in the study area. In urban parks and in forest patches on the outskirts of the Krakow city, red fox, roe deer and wild boar are found. In Wieliczka, common animals include red deer (Cervus    www.nature.com/scientificreports/ authors of this study, are a professional and highly regulated company, whose staff have considerable scientific expertise in working with wildlife, in particular AVCs. In the study area, AVC incidents must legally be reported immediately, especially in the densely inhabited areas. KABAN Co. officers received phone calls from these institutions about AVC and verified each reported incident by visiting the locations followed by undertaking on-site appropriate procedure e.g., translocating the wounded animal to animal shelter, carcass removal. The reporting time between an AVC incident and the phone calls to KABAN Co was approximately 30 min (Maciej Lesiak personal information). The details of each AVC including the date and location of the incident, the animal species (except for bird species) and their numbers, incident characteristics and the reporting time were recorded in a database (Table S1). Occasionally, multiple records of the same species were reported, for example, if there was more than one individual of the same species being hit on the same day and place. Records of domestic animals were removed from the database. The involvement of KABAN in this study enabled high quality and reliable data collection on AVC, under regulation and legal contractual obligation. Thus, consistent and reliable data on AVCs were available for this investigation, and while we cannot rule out sampling errors or biases per se, this study has provided valuable data on the scale and number of species involved in AVCs in this area, and factors underlying variations in AVC that can provide the basis for mitigation and appropriate management.
In original data received from KABAN, the exact point locations were available only for 48% of the AVCs. However, for many entries additional, qualitative location descriptors were available, allowing as to manually assign an accurate location. Due to this procedure, the percentage of AVCs with accurate location increased to 56%. The remaining AVCs were geolocated to street segments, based on street names, which were provided in all reports. Street network data were obtained from the OpenStreetMap (accessed on 29.11.2020), with main streets defined as those of primary, secondary and tertiary road type based on OpenStreetMap typology. The study area was further divided the study area into smaller regions (n = 533), where each region contained a main street with smaller streets connected to it and their neighbouring areas (defined using closest Euclidean proximity rule). To be able to investigate changes in spatial pattern of AVCs, the AVC data was aggregated within these regions with regard to year (2019 and 2020) and month (January-February, March-May, and June). June was analysed separately to detect any changes in AVC after strict lockdown was lifted.
Traffic volume data (i.e., number of vehicles per hour) were available only from Krakow and were obtained from the Department of City Traffic through the light detection system installed at major roads for the city of Krakow. The detection system counted the number of vehicles crossing the road on 19 major roads connecting the entire city for the study period on an hourly basis (Table S2).
Statistical analysis. AVC data relating to the composition of road killed species were analysed using unconstrained (Multidimensional Scaling or, MDS) and constrained (Canonical Correlation Analysis or CCA) ordination methods. Ordination methods are used for multivariate data and can determine differences between samples in a graphical manner. Unconstrained ordination is useful for viewing overall variation in the data (i.e., to represent, the pairwise dissimilarity between objects), whereas constrained ordinations reveal variation of a fixed factor(s) by minimising the effect size of the random factors 60 . All data analyses were performed in the R environment 61 , using tidyverse 62 , Vegan 63 and RVAideMemoire 64 packages. All packages and dependencies were encapsulated in an anaconda environment at https:// github. com/ SAYAN TANI26/ Proje ctAVC/. The detailed data stratification and workflow is available in Fig. 7.

AVC patterns in urban versus suburban areas.
To assess the impact of lockdown months associated with COVID-19 on AVC, the AVC data were stratified by location (understood as commune, i.e., Krakow, Wieliczka and Niepolomice), month (January, February, March, April, May, and June), and year (2019 and 2020) (see for data stratification Fig. 7). The stratified data was normalised and scaled to 1 for all further analysis. The MDS analyses were performed by computing the dissimilarity in AVC using Jaccard index 65 . The dataset was further estimated by CCA using location as a constrained variable and conditioned by year. Statistical significance (p ≤ 0.05) of locations were determined by performing PERMANOVA and the variation within location by pairwise PERMANOVA (over 1000 permutations). The p-value for the pairwise analysis was adjusted using the Benjamini-Hochberg (BH) procedure. Next, we represented the AVC patterns for each species (mammals and birds) using heat maps for visual inspection. Heat maps enabled visualisation of the intensity (high or low) of AVC for each species in the three locations. Finally, by generalised additive model (GAM) using Poisson distribution (Eq. 1), we analysed the mean difference of AVC between locations and lockdown (fixed factors) (Eq. 2). The statistical significance was computed by conducting Tukey's Honest Significance Difference (HSD) test and the p-value was adjusted using the BH procedure. Animal species that had a mean difference with false discovery rate (FDR) ≤ 0.05 were considered to be significant and the percentage of AVC for those animals was represented in boxplots.
To assess the AVC on an hourly scale, data were grouped by summing the AVC reports for each day and each month within the 24-h (h) time period of the corresponding location (commune). The time period was divided into six intervals of 4 h time periods (00:00-04:00 h, 04:01-08:00 h, 08:01-12:00 h, 12:01-16:00 h, 16:01-20:00 h and 20:01-00:00 h). Additionally, the total AVC across time was estimated by stratifying month, year and location www.nature.com/scientificreports/ (see for data stratification Fig. 7). The trend of variation in total AVC along the time range was represented by fitting the total AVC by the locally estimated scatterplot smoothing (LOESS) method.
Traffic in Krakow and its impact on AVC. Finally, to assess the influence of traffic volume (i.e., number of vehicles per hour) on AVC during the lockdown, the traffic and the AVC dataset (stratified by month and 24 h time period) for Krakow were integrated to analyse the association of vehicle movement and its impact on AVC. Traffic volume data was normalised by dividing the number of vehicles (per hour) by 1 000 000 (in million) to optimise the mean differences within the range of |0-1|. Thus, a difference of − 0.01 would correspond to a decrease of 10 000 vehicles of corresponding months in 2020 in comparison to 2019. We independently compared the datasets for each parameter (animal species, total AVC and traffic volume) within Krakow between the same months of 2019 and 2020 (fixed factor) and lockdown. Using GAM, the effect of lockdown and year for each parameter was analysed and the statistical significance was reported (same as in 3.2.1; Eq. 3). The relationship between traffic volume (converted to per million within 24 h time period) and AVC was determined by conducting Spearman's rank correlation (rho) on the respective mean difference.

Data accessibility
Datasets of all spectra shown are available in: R scripts used to generate figures are available at: https:// github. com/ SAYAN TANI26/ Proje ctAVC.  www.nature.com/scientificreports/